Delete loading script

labels.py

@@ -1,93 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Lint as: python3
-"""AG News topic classification dataset."""
-
-
-import csv
-
-import datasets
-from datasets.tasks import TextClassification
-
-
-_DESCRIPTION = """\
-AG is a collection of more than 1 million news articles. News articles have been
-gathered from more than 2000 news sources by ComeToMyHead in more than 1 year of
-activity. ComeToMyHead is an academic news search engine which has been running
-since July, 2004. The dataset is provided by the academic comunity for research
-purposes in data mining (clustering, classification, etc), information retrieval
-(ranking, search, etc), xml, data compression, data streaming, and any other
-non-commercial activity. For more information, please refer to the link
-http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html .
-The AG's news topic classification dataset is constructed by Xiang Zhang
-(xiang.zhang@nyu.edu) from the dataset above. It is used as a text
-classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann
-LeCun. Character-level Convolutional Networks for Text Classification. Advances
-in Neural Information Processing Systems 28 (NIPS 2015).
-"""
-
-_CITATION = """\
-@inproceedings{Zhang2015CharacterlevelCN,
-  title={Character-level Convolutional Networks for Text Classification},
-  author={Xiang Zhang and Junbo Jake Zhao and Yann LeCun},
-  booktitle={NIPS},
-  year={2015}
-}
-"""
-
-_TRAIN_DOWNLOAD_URL = "https://raw.githubusercontent.com/mhjabreel/CharCnn_Keras/master/data/ag_news_csv/train.csv"
-_TEST_DOWNLOAD_URL = "https://raw.githubusercontent.com/mhjabreel/CharCnn_Keras/master/data/ag_news_csv/test.csv"
-
-
-class AGNews(datasets.GeneratorBasedBuilder):
-    """AG News topic classification dataset."""
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "text": datasets.Value("string"),
-                    "label": datasets.features.ClassLabel(names=["World", "Sports", "Business", "Sci/Tech"]),
-                }
-            ),
-            homepage="http://groups.di.unipi.it/~gulli/AG_corpus_of_news_articles.html",
-            citation=_CITATION,
-            task_templates=[TextClassification(text_column="text", label_column="label")],
-        )
-
-    def _split_generators(self, dl_manager):
-        train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
-        test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL)
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
-        ]
-
-    def _generate_examples(self, filepath):
-        """Generate AG News examples."""
-        with open(filepath, encoding="utf-8") as csv_file:
-            csv_reader = csv.reader(
-                csv_file, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True
-            )
-            for id_, row in enumerate(csv_reader):
-                label, title, description = row
-                # Original labels are [1, 2, 3, 4] ->
-                #                   ['World', 'Sports', 'Business', 'Sci/Tech']
-                # Re-map to [0, 1, 2, 3].
-                label = int(label) - 1
-                text = " ".join((title, description))
-                yield id_, {"text": text, "label": label}